Machine learning platform for processing data maps

ABSTRACT

A system, method and program product for implementing a machine learning platform that processes a data map having feature and operational information. A system is disclosed that includes an interpretable machine learning model that generates a function in response to an inputted data map, wherein the data map includes feature data and operational data over a region of interest, and wherein the function relates a set of predictive variables to one or more response variables; an integration/interpolation system that generates the data map from a set of disparate data sources; and an analysis system that evaluates the function to predict outcomes at unique points in the region of interest.

TECHNICAL FIELD

The subject matter of this invention relates to machine learningplatforms, and more particularly to a machine learning platform thatprocess continuous data maps to evaluate and predict outcomes in domainssuch as oil and gas exploration.

BACKGROUND

There exist numerous domains in which huge amounts of data are generatedon a daily basis. Often, the data may be captured from differentsources, involve different purposes, and be stored in differentdatabases. The ability to use such data in a comprehensive manner topredict outcomes remains an ongoing challenge.

For example, in the field of oil/gas/water exploration, large amounts ofgeological data and production data are generated on a daily basis fromdifferent sources including both conventional and unconventional (e.g.,shale) reservoirs. In this domain, determining where to drill, i.e.,sweet spot identification techniques, relies on only limited aspects ofthe collected data such as geology, seismic data analysis and/or expertknowledge. This unfortunately often results in poor selections that arebased on traditional physics models that do not allow for acomprehensive utilization of all data sources.

SUMMARY

Aspects of the disclosure provide an improved machine learning platformthat generates and processes data maps to evaluate and predict outcomes.A data map can be obtained, integrated and interpolated from variousdata sources for a region of interest. An interpretable machine learningmodel can be utilized to generate a function that relates a set ofpredictive variables to one or more response variables. The function canbe evaluated by experts to alter models and be utilized to predictoutcomes within the region of interest.

In some aspects, Generalized Additive Models (GAMs) with shapeconstraints can be utilized, which are a class of interpretable machinelearning models, for tasks that include: 1) encoding expert knowledgeabout the shape of the effects of the predictive variables and encodingthe interaction among those variables; 2) quantifying the effects of thepredictive variables on operations; and 3) predicting the outcome atselected locations.

In one aspect, the machine learning platform is utilized to processgeological data involving unconventional reservoirs (i.e., shale),including production data and completion parameters with the aim toperform sweet spot identification. The platform provides an end-to-enddata-driven solution that preprocesses and performs feature engineeringof geological data and integrates those features with production dataand completions. This solution can support geologists and engineers ondecisions about where to drill new wells in the reservoirs and/or assistthem to analyze the impact of geological data and completions on theproduction of reservoirs.

An aspect discloses a machine learning platform adapted to assist in oiland gas exploration, comprising: an interpretable machine learning modelthat generates a function in response to an inputted data map, whereinthe data map includes geophysical data and operational data over aregion of interest, and wherein the function relates a set of predictivevariables to one or more response variables; anintegration/interpolation system that generates the data map from a setof disparate data sources that includes horizontal well logs, verticalwell logs and production data; and an analysis system that evaluates thefunction to predict outcomes at unique points in the region of interest.

A further aspect discloses a computer program product stored on acomputer readable medium, which when executed by a computing systemprovides a machine learning platform to assist in oil and gasexploration, the program product comprising: program code forimplementing an interpretable machine learning model that provides afunction in response to an inputted data map, wherein the data mapincludes geophysical data and operational data over a region ofinterest, and wherein the function relates a set of predictive variablesto one or more response variables; program code that generates the datamap from a set of disparate data sources in which the set of disparatedata sources are integrated and interpolated to provide a continuous setof data over the region of interest; and program code that evaluates thefunction to predict outcomes at unique points in the region of interest.

A third aspect discloses a method of using a machine learning platformto perform sweet spotting, including: integrating feature data fromhorizontal well logs and vertical well logs to form a set of integratedfeature data; interpolating the feature data over a region of interestto generate a data map; integrating operational data into the data map,wherein the operational data includes production, completion andengineering data at different points in the region of interest;inputting the data map into a machine learning model to generate afunction, wherein the function relates a set of predictive variables toone or more response variables; and analyzing the function to identify asweet spot in the region of interest.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of this invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings in which:

FIG. 1 shows a machine learning platform according to embodiments.

FIG. 2 shows a sweet spotting system that employs the machine learningplatform of FIG. 1 according to embodiments.

FIG. 3 shows an interpolated data map according to embodiments.

FIG. 4 depicts a GUI for integrating feature data from different sourcesaccording to embodiments.

FIG. 5 depicts a GUI showing a GAMs model according to embodiments.

FIG. 6 depicts a GUI showing a further GAMs model according toembodiments.

FIG. 7 depicts a GUI showing a SCAMs model according to embodiments.

FIG. 8 depicts a GUI showing an ML (machine learning) model according toembodiments.

FIG. 9 shows a computing system for implementing the machine learningplatform of FIG. 1 for oil/gas exploration.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention, and therefore should not be considered aslimiting the scope of the invention. In the drawings, like numberingrepresents like elements.

DETAILED DESCRIPTION

Referring now to the drawings, FIG. 1 depicts a machine learningplatform 10 that includes an interpretable machine learning model 20 toprocess a multi-dimensional continuous data map (“data map”) 12. Datamap 12 can be embodied as a grid of data over a region of interest inwhich each point in the grid includes one or more data values. Data map12 can represent any information domain, e.g., a physical domain such asoil/gas exploration, a virtual domain such as digital marketing, etc. Assuch, machine learning platform 10 is intended to provide technologyimprovements in the field of machine learning systems.

Data map 12 includes both feature data 21 and operational data 23obtained from a set of disparate data sources 16. Feature data 21 refersto information that describes attributes of different points in theregion of interest. For example, in a physical domain, feature data 21can comprise geophysical attributes, agricultural information, realestate data, etc. In a virtual domain, feature data 21 can includeproduct information, CRM (customer relationship management) information,etc. Operational data 23 refers to information that describesactivities, operations, processes, performance, etc., associated withdifferent points in the region of interest. For example, in a physicaldomain, operational data can comprise production results, engineeringrequirements, costs, etc. In a virtual domain, operational data caninclude sales data, click thru data, marketing efforts, profit, etc.

Data map 12 can be generated by an integration/interpolation system 14that processes information from the various data sources 16. Integrationcan for example involve combining feature data 21 from differentdatabases that fall within the same region of interest. Interpolationprocesses discrete points of data to create a continuous data map 12 inwhich each point on the data map is associated with at least one datavalue. In one embodiment, interpolation is performed on feature data 21to create the continuous data map 12, to which operational data is thenintegrated.

Once generated, data map 12 is inputted into interpretable machinelanguage learning model 20, which generates one or more functions 24.Each function 24 relates a set of predictive variables, such as physicalfeatures, completions, engineering requirements, etc., to one or moreresponse variables, such as production. An illustrative function can bein the form:

g(E(Y))=β+f ₁(x ₁)+f ₂(x ₂) . . . f _(m)(x _(m))

where Y is the response variable, x₁, x₂, etc., are predictivevariables, and f₁, f₂, etc., are weights, function, or other models.

Interpretable machine language learning model 20 can be trained by atraining system 18 that for example uses previously collected data mapsand results in the particular domain. Because the output of theinterpretable machine language learning model 20 is a function 24, anexpert 22 can review and modify the function 24 based on domainknowledge, and feed that domain knowledge constraints into the model 20.For example, the expert may know that a certain pair of predictivevariables generally rise or fall in an inverse manner. If the function24 indicates something else, the expert 22 can update the model 20.

In an alternative embodiment, one or more classical or black box machinelearning models can be utilized in place of, or in addition to, theinterpretable machine learning model 20.

Once the function 24 is generated, analysis system 26 can then evaluatethe function 24 to predict outcomes in the region of interest 28 atdifferent locations. Evaluation of the function can be handled through agraphical user interface (GUI) and can for example include identifyingsweet spots, performing what-if scenarios, discovering outliers, etc.

FIG. 2 depicts an application of the machine learning platform 10 ofFIG. 1 that performs sweet spotting in a region of interest system in anoil/gas/water exploration domain. As shown, sweet spotting system 60generally uses feature data from horizontal well logs 30, vertical welllogs 31, operational data 48 and expert knowledge 50 to provide anend-to-end data driven process to allow for the analysis of potentialdrilling locations, the impact of geological data and completions on theproduction of reservoirs, costs related to completions, etc., by endusers 52, such as geologists and engineers.

Horizontal well logs 30 and vertical well logs 31 generally comprisegeophysical measurements, such as gamma ray data, neutron porosity,density, etc., of a target geological formation surrounding a well. Thewell log data for a region of interest is initially collected andprocessed by log processing system 32, which, e.g., collects well logsfrom LAS (logic ASCII standard) files and identifies target formations,extracts respective well log sections, and identifies well parameters(e.g., top and bottom well sections, deviation angles, directionalpaths, actual depths, measured depths, well locations, etc.). Inaddition, geological measurements are extracted and preprocessed to,e.g., remove outliers, etc. In some case, horizontal well logs 30 caninclude some production information as well.

Once collected, the information is fed into geophysical data integrationsystem 34, which integrates geological measurements from the horizontalwell logs 30 and vertical well logs 31. Integration of the vertical welllogs 31 begins with identifying geological measurements around a wellwhich are summarized with representative values, e.g., statistics suchas moments or empirical quantiles of the distribution. Integration ofthe horizontal well logs 30 begins with, e.g., performing a downsampling of a smoothing approximation of the physical measurements toobtain representative values across the path of the well. Once obtained,geological data integration system 34 joins the representative valuesfrom vertical wells and horizontal wells to create an expanded datasource for input to interpolation system 36. Empirical distributions(e.g., using QQ plots or hypothesis testing) from both sources can becompared to validate the viability of integration.

Interpolation system 26 utilizes the integrated geophysical data toestimate the unknown geological measurements across a region ofinterest, e.g., around production data locations. In one approach, localinterpolation such as local-kriging (i.e., kriging around a vicinity)can be utilized which is useful to reduce computational overhead. Usingthis approach, kriging parameters are estimated from the data to providea data-driven solution to obtaining estimated geophysical data for theregion of interest. For example, FIG. 3 shows a mapping with actualintegrated horizontal and vertical geophysical data 37 on the left handside and interpolated geophysical data map 38 over a region of intereston the right hand side. Each point in the left plot indicates ahorizontal/vertical wellhead position. Shading and/or color valuesindicate the petrophysical associated values (e.g., gamma-rays values).The right plot shows the interpolated results (the map) using kriging.The result is a continuous geophysical feature gridded map based on theinterpolated physical features providing a proxy for geology.

Referring again to FIG. 2, once generated, the interpolated geophysicaldata map 38 is integrated with operational data 48 by logdata/production integration system 40. System 40 integrates thegeological proxy with production parameters (e.g., intensity ofcompletion, fracturing pressure by length, total length of a horizontalwell, etc.) to create an integrated log/production data map 42 for theregion of interest.

Next the integrated log/production data map 42 is fed into a predictivemodeling system 44. In one approach, an interpretable machine learningmodel is utilized which allows for the inclusion of expert knowledge 50in the model. The expert knowledge 50 can be included into aninterpretable machine learning model via functions such as convex,concave, monotonic increasing/decreasing, linear, etc., via sets, fuzzysets, probability distributions, mathematical expressions, etc.

One illustrative interpretable machine learning model includes aGeneralized Additive Model (GAM) with shape constraints. Thus, if anengineer (i.e., expert) knows that a variable has a monotonic concavedecreasing relationship with production, then the knowledge can be codedinto the GAMs using shape constraints on the GAMs spline. In a furtherapproach, multiple models can be used, e.g., GAMs, random forest,support vector machines, Gaussian process, etc., and ranked according topredefined metrics, e.g., RMSE, MAE, MAS, etc., for sweet spotprediction.

Finally, effect analysis system 46 can be utilized by an end user 52 (orsome other system) to predict outcomes in the region of interest. Forexample, GAMs with shape constraints can be used for discovering andcharacterize the main effects of explanatory values such as geologicalfeatures, completions, and well locations on production. The analysiscan include the use or generation of effect plots, partial residualplots, functions, etc. The resulting output can include drillinglocations given by prediction maps indicating areas of highestprobability for success.

A graphical user interface (GUI) can be employed that includes, e.g., aselector of vertical information attributes and horizontal informationattributes; a selector of information attributes of production data; aselector of information attributes of engineering variables; a controlfor processing data, data exploration, correlation estimation, andcorrelation range; a control for geophysical data integration; aselector of interpolation models, and control for applying interpolationon the integrated geophysical data; a display for visualization ofinterpolation maps, statistics and data exploration of the interpolatedgeophysical data; a control for data integration of the interpolatedgeophysical data, production data and reservoir variables; a selector ofmachine learning models; a selector of interpretable machine learningmodels; a control allowing input of prior/expert knowledge intointerpretable machine learning models; a control for fitting models; adisplay for visualization of prediction maps, prediction statistics frommachine learning models over locations of interest; and a display forvisualization/analysis of effects/behavior of predictive variables onproduction data on locations of interest.

FIG. 4 depicts a GUI showing the integration of horizontal well datawith vertical well data. FIG. 5 depicts a GUI in which the user canselect and modify a model function. Namely, FIG. 5 shows how priorknowledge about the interaction among predictors can be incorporatedinto the ML model. In this case, assume the user knows beforehand thatthe predictors “surface_X” and “surface_Y” are interacting. The user canmodel that interaction with a smooth function (technically, GAMs usetensor product splines for that purpose). The modeling equation would begiven by y=te(surface_X,surface_Y). Because the ML model is an additivemodel it is possible to add more terms to the equation, e.g.,y=te(surface_X,surface_Y)+(new term) . . . +(new term) by replacing“none” in one or more drop down boxes with additionalpredictors/interaction terms. The four plots on the right show fourdifferent views of the same effect of the interaction termte(surface_X,surface_Y) on the predicted variable y (in this case thelogarithm of the twelve-months of cumulative production of oil). Theeffect of the interaction term “te(surface_X,surface_Y)” is modeled as a3D surface.

FIG. 6 depicts a GUI showing a different GAMs model. In this case, theuser models the predicted variable as a function of two(non-interaction) predictors: proppant intensity and completed laterallength. The only prior knowledge that the user has in this case is thatthere is no interaction between the variables, so the model then will begiven by y=s(proppantIntensity)+s(completedLateralLength). The GAMsmodel then fits smooth functions (in this case regularized splines) toeach predictor. Finally, the trained GAMs will plot the effect ofproppant intensity on the predicted variable and the effect of completedlateral length on the predicted variable, as shown on the right handside of FIG. 6. Technically, these plots are called “conditional plots”and a user can use them to explain the relationship between apredictor/interaction term and the predicted variable keeping the othervariables to some fixed value. For example, the top plot shows therelationship between proppantIntensity and the predicted variable, giventhat the completed lateral length variable is fixed to its mean value.

FIG. 7 depicts a similar GUI, but in this case, a Shape ConstraintAdditive Model (SCAMs) is utilized in which the user can include priorknowledge about the relationship between predictors and the predictedvariable. For example, the user can include prior knowledge about therelationship between proppant intensity and the predicted oilproduction, e.g., that the relationship is better modeled by a monotonicincreasing and concave function. That codification is given in thedropdown box with the expression bs=“mpi”. Because the model isadditive, more terms can be added including interaction terms, withshape constraints (e.g., bs=“mpi”), or terms without constraints (e.g.,traditional GAMs). In the depicted example, the user is modeling GR(gamma-ray) without constraints, that is, the user has no priorknowledge about the relationship between GR and the predicted value.

FIG. 8 depicts a GUI showing a machine learning (ML) model (in this casea linear regression model). In this example, a method is selected (e.g.,support vector machines, random forest, linear regression, etc.) andspecific plots for those models are provided. For example, if the userselects a random forest, variable importance is provided. If the userselects a support vector machine, support vectors plots are provided.The graphs basically shows four different plot for the linear regressionmodel that would help users to better understand the role of this modelfor predictive modeling purposes.

An example use case of the sweet spotting system of FIG. 2 is asfollows. Gamma-ray logs are obtained from horizontal 30 and verticalwell logs 31 (not necessarily from production wells). The gamma-ray logsobtained from horizontal 30 and vertical well logs 31 are integrated andinterpolated using local-kriging into a data map. The horizontal 30 andvertical well logs 31 are first preprocessed, e.g., to eliminate anyoutlier data. A smoothing approximation obtained from horizontal welllogs are then down sampled to obtain a set of values. Median values areobtained from the vertical well logs 31. The values are then integratedtogether to form a grid of geological features (i.e., a data map).

Once the grid of geological features is computed, those features areintegrated with other production data variables. Production data isobtained from a separate data source that includes completionsparameters (e.g., completed lateral length and proppant intensity) fromproduction wells, shut-in production days from production wells,well-locations (x,y coordinates) of production wells, and cumulative oiland gas production.

The resulting data map is then used in machine learning models such as aGaussian process, Support Vector Machines, Random forest, Neuralnetworks, GAMs, etc. GAMs is utilized with shape constraints to allowfor the inclusion prior expert knowledge on the shape and effects of thepredictive variables on production. For example, an expert may constrainthe effect of proppant intensity to be monotonic increasing and convex,while, constraining the shut-in production days to be monotonicdecreasing and concave. Additionally, the expert can code the variableinteraction using high order splines, for example using a tensor productspline to code the interaction between the x and y well-coordinates. TheGAM's output will be a set of functions that quantify the effects of thepredictive variables on production. For example, by analyzing the tensorproduct spline related to those variables, the effect of well locationson production can be determined.

It is understood that the machine learning platform 10 may beimplemented as a computer program product stored on a computer readablestorage medium. The computer readable storage medium can be a tangibledevice that can retain and store instructions for use by an instructionexecution device. The computer readable storage medium may be, forexample, but is not limited to, an electronic storage device, a magneticstorage device, an optical storage device, an electromagnetic storagedevice, a semiconductor storage device, or any suitable combination ofthe foregoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Python, Smalltalk, C++ orthe like, and conventional procedural programming languages, such as the“C” programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 9 shows a computing system 50 having a machine learning platform 62adapted for oil/gas exploration, which may comprise any type ofcomputing device and for example includes at least one processor 52,memory 60, an input/output (I/O) 54 (e.g., one or more I/O interfacesand/or devices), and a communications pathway 55. In general,processor(s) execute program code which is at least partially fixed inmemory. While executing program code, processor(s) can process data,which can result in reading and/or writing transformed data from/tomemory and/or I/O for further processing, including databases 64. Thepathway provides a communications link between each of the components incomputing system. I/O can comprise one or more human I/O devices, whichenable a user to interact with computing system. Computing system mayalso be implemented in a distributed manner such that differentcomponents reside in different physical locations.

Furthermore, it is understood that the machine learning platform orrelevant components thereof (such as an API component, agents, etc.) mayalso be automatically or semi-automatically deployed into a computersystem by sending the components to a central server or a group ofcentral servers. The components are then downloaded into a targetcomputer that will execute the components. The components are theneither detached to a directory or loaded into a directory that executesa program that detaches the components into a directory. Anotheralternative is to send the components directly to a directory on aclient computer hard drive. When there are proxy servers, the processwill select the proxy server code, determine on which computers to placethe proxy servers' code, transmit the proxy server code, then installthe proxy server code on the proxy computer. The components will betransmitted to the proxy server and then it will be stored on the proxyserver.

The foregoing description of various aspects of the invention has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdisclosed, and obviously, many modifications and variations arepossible. Such modifications and variations that may be apparent to anindividual in the art are included within the scope of the invention asdefined by the accompanying claims.

What is claimed is:
 1. A machine learning platform adapted to assist inoil and gas exploration, comprising: an interpretable machine learningmodel that generates a function in response to an inputted data map,wherein the data map includes geophysical data and operational data overa region of interest, and wherein the function relates a set ofpredictive variables to one or more response variables; anintegration/interpolation system that generates the data map from a setof disparate data sources that includes horizontal well logs, verticalwell logs and production data; and an analysis system that evaluates thefunction to predict outcomes at unique points in the region of interest.2. The machine learning platform of claim 1, wherein the data mapincludes at least one data value at every point in the region ofinterest.
 3. The machine learning platform of claim 1, wherein thegeophysical data includes at least one of gamma ray, neutron porosity,or density measurements at different points in the region of interest.4. The machine learning platform of claim 3, wherein the operationaldata describes production data and engineering data at different pointsin the region of interest.
 5. The machine learning platform of claim 1,wherein integration/interpolation system: integrates data fromhorizontal well logs and vertical well logs to form an integrated dataset; interpolates the integrated data set into a data map of continuousdata; and integrates operational data into the data map.
 6. The machinelearning platform of claim 1, wherein the response variable representsproduction.
 7. The machine learning platform of claim 1, wherein theoutcomes comprise sweet spot locations for locating one of oil, gas orwater.
 8. The machine learning platform of claim 1, further comprising aGUI for displaying and modifying the function.
 9. A computer programproduct stored on a computer readable medium, which when executed by acomputing system provides a machine learning platform to assist in oiland gas exploration, the program product comprising: program code forimplementing an interpretable machine learning model that provides afunction in response to an inputted data map, wherein the data mapincludes geophysical data and operational data over a region ofinterest, and wherein the function relates a set of predictive variablesto one or more response variables; program code that generates the datamap from a set of disparate data sources in which the set of disparatedata sources are integrated and interpolated to provide a continuous setof data over the region of interest; and program code that evaluates thefunction to predict outcomes at unique points in the region of interest.10. The program product of claim 9, wherein the set of disparate datasources include horizontal well logs and vertical well logs.
 11. Theprogram product of claim 9, wherein the program code that generates thedata map: integrates data from horizontal well logs and vertical welllogs to form an integrated data set; interpolates the integrated dataset into the data map; and integrates operational data into the datamap.
 12. The program product of claim 11, wherein the operational dataincludes production data and engineering data at different points in theregion of interest.
 13. The program product of claim 9, wherein thegeophysical data includes at least one of gamma ray, neutron porosity,or density measurements at different points in the region of interest.14. The program product of claim 13, wherein the response variablerepresents production.
 15. The program product of claim 14, wherein theoutcomes comprise sweet spot locations for locating one of oil, gas orwater.
 16. The program product of claim 9, further comprising programcode to generate a GUI for displaying and modifying the function.
 17. Amethod of using a machine learning platform to perform sweet spotting,comprising: integrating feature data from horizontal well logs andvertical well logs to form a set of integrated feature data;interpolating the feature data over a region of interest to generate adata map; integrating operational data into the data map, wherein theoperational data includes production, completion and engineering data atdifferent points in the region of interest; inputting the data map intoa machine learning model to generate a function, wherein the functionrelates a set of predictive variables to one or more response variables;and analyzing the function to identify a sweet spot in the region ofinterest.
 18. The method of claim 17, wherein the response variablerepresents production.
 19. The method of claim 17, further comprisingproviding a GUI for displaying and modifying the function.
 20. Themethod of claim 17, wherein the feature data describes geophysicalattributes at different points in the region of interest.